Instructions to use aieng-lab/codet5p-220m_review-aspect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aieng-lab/codet5p-220m_review-aspect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aieng-lab/codet5p-220m_review-aspect")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aieng-lab/codet5p-220m_review-aspect") model = AutoModelForSequenceClassification.from_pretrained("aieng-lab/codet5p-220m_review-aspect") - Notebooks
- Google Colab
- Kaggle
CodeT5+ 220m for classifying API reviews
This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'.
- Developed by: Fabian C. Peña, Steffen Herbold
- Finetuned from: Salesforce/codet5p-220m
- Replication kit: https://github.com/aieng-lab/senlp-benchmark
- Language: English
- License: MIT
Citation
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
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Model tree for aieng-lab/codet5p-220m_review-aspect
Base model
Salesforce/codet5p-220m